Real estate price prediction enables informed decision-making in markets by accurately modeling spatial linkages and market patterns. This study employs DBSCAN clustering with the Haversine formula to calculate geodesic distances and identify clusters within a 10-km radius. A hybrid method computes cluster average pricing, using the median for high-variance clusters and the mean for low-variance clusters. Machine learning models such as Decision Tree, Random Forest, and XGBoost are trained using the generated features and normalized data for real estate price prediction. The best-performing model achieves an accuracy of less than 1% MAPE (Mean Absolute Percentage Error), ensuring reliability. To enhance usability, the proposed system integrates an intuitive user interface for real-time predictions, making it accessible to a broad audience. By combining machine learning, feature engineering, and geographic clustering, this approach significantly improves real estate valuation accuracy.